Since the mid-20th century, communication researchers have recognized that audience members selectively expose themselves to information and opinions congenial to their pre-existing views. While this was a controversial idea during the broadcast era of mass media, the expansion of media choice on television and use of information communication technology has brought increased attention to selectivity among audience members. Contemporary scholarship investigates the extent to which people select proattitudinal information or avoid counterattitudinal information and the role these choices play in the effects of media messages on viewers. While selective exposure is a broader phenomenon, this article substantively focuses on the use of politically partisan media, especially the research methods used to investigate media selectivity and its effects. This literature manifests an increased attention to measurement, especially how we measure the core concept of media exposure, novel experimental designs intended to allow investigators to directly view individual choice behavior in complex media environments, and attention to new sources of large-scale data from social media and large text samples. Scholars agree that partisan websites and cable networks provide content politically distinct enough to allow viewers to segregate themselves into liberal and conservative audiences for news but that this kind of polarized viewing is only part of how viewers use media today. A nuanced picture of selectivity shows audiences selecting congenial content but employing broader media use repertoires as well. The mechanisms and effects of media selectivity are psychologically complex and sensitive to contextual factors such as the political issue under consideration.
Kevin Arceneaux and Martin Johnson
Bradford William Hesse
The presence of large-scale data systems can be felt, consciously or not, in almost every facet of modern life, whether through the simple act of selecting travel options online, purchasing products from online retailers, or navigating through the streets of an unfamiliar neighborhood using global positioning system (GPS) mapping. These systems operate through the momentum of big data, a term introduced by data scientists to describe a data-rich environment enabled by a superconvergence of advanced computer-processing speeds and storage capacities; advanced connectivity between people and devices through the Internet; the ubiquity of smart, mobile devices and wireless sensors; and the creation of accelerated data flows among systems in the global economy. Some researchers have suggested that big data represents the so-called fourth paradigm in science, wherein the first paradigm was marked by the evolution of the experimental method, the second was brought about by the maturation of theory, the third was marked by an evolution of statistical methodology as enabled by computational technology, while the fourth extended the benefits of the first three, but also enabled the application of novel machine-learning approaches to an evidence stream that exists in high volume, high velocity, high variety, and differing levels of veracity. In public health and medicine, the emergence of big data capabilities has followed naturally from the expansion of data streams from genome sequencing, protein identification, environmental surveillance, and passive patient sensing. In 2001, the National Committee on Vital and Health Statistics published a road map for connecting these evidence streams to each other through a national health information infrastructure. Since then, the road map has spurred national investments in electronic health records (EHRs) and motivated the integration of public surveillance data into analytic platforms for health situational awareness. More recently, the boom in consumer-oriented mobile applications and wireless medical sensing devices has opened up the possibility for mining new data flows directly from altruistic patients. In the broader public communication sphere, the ability to mine the digital traces of conversation on social media presents an opportunity to apply advanced machine learning algorithms as a way of tracking the diffusion of risk communication messages. In addition to utilizing big data for improving the scientific knowledge base in risk communication, there will be a need for health communication scientists and practitioners to work as part of interdisciplinary teams to improve the interfaces to these data for professionals and the public. Too much data, presented in disorganized ways, can lead to what some have referred to as “data smog.” Much work will be needed for understanding how to turn big data into knowledge, and just as important, how to turn data-informed knowledge into action.
Communication research has recently had an influx of groundbreaking findings based on big data. Examples include not only analyses of Twitter, Wikipedia, and Facebook, but also of search engine and smartphone uses. These can be put together under the label “digital media.” This article reviews some of the main findings of this research, emphasizing how big data findings contribute to existing theories and findings in communication research, which have so far been lacking. To do this, an analytical framework will be developed concerning the sources of digital data and how they relate to the pertinent media. This framework shows how data sources support making statements about the relation between digital media and social change. It is also possible to distinguish between a number of subfields that big data studies contribute to, including political communication, social network analysis, and mobile communication. One of the major challenges is that most of this research does not fall into the two main traditions in the study of communication, mass and interpersonal communication. This is readily apparent for media like Twitter and Facebook, where messages are often distributed in groups rather than broadcast or shared between only two people. This challenge also applies, for example, to the use of search engines, where the technology can tailor results to particular users or groups (this has been labeled the “filter bubble” effect). The framework is used to locate and integrate big data findings in the landscape of communication research, and thus to provide a guide to this emerging area.
Dal Yong Jin
Political economy of the media includes several domains including journalism, broadcasting, advertising, and information and communication technology. A political economy approach analyzes the power relationships between politics, mediation, and economics. First, there is a need to identify the intellectual history of the field, focusing on the establishment and growth of the political economy of media as an academic field. Second is the discussion of the epistemology of the field by emphasizing several major characteristics that differentiate it from other approaches within media and communication research. Third, there needs an understanding of the regulations affecting information and communication technologies (ICTs) and/or the digital media-driven communication environment, especially charting the beginnings of political economy studies of media within the culture industry. In particular, what are the ways political economists develop and use political economy in digital media and the new media milieu driven by platform technologies in the three new areas of digital platforms, big data, and digital labor. These areas are crucial for analysis not only because they are intricately connected, but also because they have become massive, major parts of modern capitalism.